Evidence map›Paper›PMID 36499318›Full record

ArticleInternational journal of molecular sciences2022

Combined Model-Based Prediction for Non-Invasive Prenatal Screening.

So-Yun Yang, Kyung Min Kang, Sook-Young Kim, Seo Young Lim, Hee Yeon Jang, Kirim Hong, Dong Hyun Cha, Sung Han Shim, Je-Gun Joung

Abstract read
In one paragraph

Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

So-Yun YangDepartment of Biomedical Science, College of Life Science, CHA University, Seongnam 13488, Republic of Korea.ORCID 0000-0001-8316-9968
Kyung Min KangCenter for Genome Diagnostics, CHA Biotech Inc., Seoul 06135, Republic of Korea.
Sook-Young KimCHA Future Medicine Research Institute, CHA Bundang Medical Center, Seongnam 13488, Republic of Korea.
Seo Young LimDepartment of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin 17035, Republic of Korea.
Hee Yeon JangDepartment of Biomedical Science, College of Life Science, CHA University, Seongnam 13488, Republic of Korea.ORCID 0000-0003-4127-4748
Kirim HongDepartment of Obstetrics and Gynecology, CHA Gangnam Medical Center, CHA University, Seoul 06135, Republic of Korea.ORCID 0000-0001-8258-9696
Dong Hyun ChaCenter for Genome Diagnostics, CHA Biotech Inc., Seoul 06135, Republic of Korea.ORCID 0000-0003-0722-1714
Sung Han ShimDepartment of Biomedical Science, College of Life Science, CHA University, Seongnam 13488, Republic of Korea.
Je-Gun JoungDepartment of Biomedical Science, College of Life Science, CHA University, Seongnam 13488, Republic of Korea.

Funding

National Research Foundation of Korea NRF-2022R1F1A1071285
6 · The paper itself

Abstract

The risk of chromosomal abnormalities in the child increases with increasing maternal age. Although non-invasive prenatal testing (NIPT) is a safe and effective prenatal screening method, the accuracy of the test results needs to be improved owing to various testing conditions. We attempted to achieve a more accurate and robust prediction of chromosomal abnormalities by combining multiple methods. Here, three different methods, namely standard Z-score, normalized chromosome value, and within-sample reference bin, were used for 1698 reference and 109 test samples of whole-genome sequencing. The logistic regression model combining the three methods achieved a higher accuracy than any single method. In conclusion, the proposed method offers a promising approach for increasing the reliability of NIPT.

Indexed as

Chromosome AberrationsPrenatal DiagnosisAneuploidyChildFemaleHumansMaternal AgePregnancyReproducibility of ResultsTrisomyclinical significancecopy number variationsnext-generation sequencingnon-invasive prenatal testingtrisomy

Identifiers

PMID36499318
PMCPMC9737181

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.